119 research outputs found
Benchmarking Multivariate Time Series Classification Algorithms
Time Series Classification (TSC) involved building predictive models for a
discrete target variable from ordered, real valued, attributes. Over recent
years, a new set of TSC algorithms have been developed which have made
significant improvement over the previous state of the art. The main focus has
been on univariate TSC, i.e. the problem where each case has a single series
and a class label. In reality, it is more common to encounter multivariate TSC
(MTSC) problems where multiple series are associated with a single label.
Despite this, much less consideration has been given to MTSC than the
univariate case. The UEA archive of 30 MTSC problems released in 2018 has made
comparison of algorithms easier. We review recently proposed bespoke MTSC
algorithms based on deep learning, shapelets and bag of words approaches. The
simplest approach to MTSC is to ensemble univariate classifiers over the
multivariate dimensions. We compare the bespoke algorithms to these dimension
independent approaches on the 26 of the 30 MTSC archive problems where the data
are all of equal length. We demonstrate that the independent ensemble of
HIVE-COTE classifiers is the most accurate, but that, unlike with univariate
classification, dynamic time warping is still competitive at MTSC.Comment: Data Min Knowl Disc (2020
Feature-based time-series analysis
This work presents an introduction to feature-based time-series analysis. The
time series as a data type is first described, along with an overview of the
interdisciplinary time-series analysis literature. I then summarize the range
of feature-based representations for time series that have been developed to
aid interpretable insights into time-series structure. Particular emphasis is
given to emerging research that facilitates wide comparison of feature-based
representations that allow us to understand the properties of a time-series
dataset that make it suited to a particular feature-based representation or
analysis algorithm. The future of time-series analysis is likely to embrace
approaches that exploit machine learning methods to partially automate human
learning to aid understanding of the complex dynamical patterns in the time
series we measure from the world.Comment: 28 pages, 9 figure
Deep learning for time series classification: a review
Time Series Classification (TSC) is an important and challenging problem in
data mining. With the increase of time series data availability, hundreds of
TSC algorithms have been proposed. Among these methods, only a few have
considered Deep Neural Networks (DNNs) to perform this task. This is surprising
as deep learning has seen very successful applications in the last years. DNNs
have indeed revolutionized the field of computer vision especially with the
advent of novel deeper architectures such as Residual and Convolutional Neural
Networks. Apart from images, sequential data such as text and audio can also be
processed with DNNs to reach state-of-the-art performance for document
classification and speech recognition. In this article, we study the current
state-of-the-art performance of deep learning algorithms for TSC by presenting
an empirical study of the most recent DNN architectures for TSC. We give an
overview of the most successful deep learning applications in various time
series domains under a unified taxonomy of DNNs for TSC. We also provide an
open source deep learning framework to the TSC community where we implemented
each of the compared approaches and evaluated them on a univariate TSC
benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By
training 8,730 deep learning models on 97 time series datasets, we propose the
most exhaustive study of DNNs for TSC to date.Comment: Accepted at Data Mining and Knowledge Discover
MTS2Graph: Interpretable Multivariate Time Series Classification with Temporal Evolving Graphs
Conventional time series classification approaches based on bags of patterns
or shapelets face significant challenges in dealing with a vast amount of
feature candidates from high-dimensional multivariate data. In contrast, deep
neural networks can learn low-dimensional features efficiently, and in
particular, Convolutional Neural Networks (CNN) have shown promising results in
classifying Multivariate Time Series (MTS) data. A key factor in the success of
deep neural networks is this astonishing expressive power. However, this power
comes at the cost of complex, black-boxed models, conflicting with the goals of
building reliable and human-understandable models. An essential criterion in
understanding such predictive deep models involves quantifying the contribution
of time-varying input variables to the classification. Hence, in this work, we
introduce a new framework for interpreting multivariate time series data by
extracting and clustering the input representative patterns that highly
activate CNN neurons. This way, we identify each signal's role and
dependencies, considering all possible combinations of signals in the MTS
input. Then, we construct a graph that captures the temporal relationship
between the extracted patterns for each layer. An effective graph merging
strategy finds the connection of each node to the previous layer's nodes.
Finally, a graph embedding algorithm generates new representations of the
created interpretable time-series features. To evaluate the performance of our
proposed framework, we run extensive experiments on eight datasets of the
UCR/UEA archive, along with HAR and PAM datasets. The experiments indicate the
benefit of our time-aware graph-based representation in MTS classification
while enriching them with more interpretability
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